scholarly journals Scene statistics and noise determine the relative arrangement of receptive field mosaics

2021 ◽  
Vol 118 (39) ◽  
pp. e2105115118
Author(s):  
Na Young Jun ◽  
Greg D. Field ◽  
John Pearson
Keyword(s):  
Neural Coding ◽  
Coding Theory ◽  
Receptive Fields ◽  
Optimal Arrangement ◽  
Efficient Coding ◽  
Natural Stimuli ◽  
Signal Stimulus ◽  
Theoretical Predictions ◽  
On And Off Pathways ◽  
Contrast Response

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet, encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide theoretical predictions for the configuration of opponent pathways in the nervous system.

scholarly journals The optimal spatial arrangement of ON and OFF receptive fields

2021 ◽  
Author(s):  
Na Young Jun ◽  
Greg Field ◽  
John Pearson
Keyword(s):  
Neural Coding ◽  
Coding Theory ◽  
Receptive Fields ◽  
Spatial Arrangement ◽  
Optimal Arrangement ◽  
Natural Stimuli ◽  
Signal Stimulus ◽  
Theoretical Predictions ◽  
On And Off Pathways ◽  
Contrast Response

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a second-order phase transition between aligned and anti-aligned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide novel theoretical predictions for the configuration of opponent pathways in the nervous system.

scholarly journals Active efficient coding explains the development of binocular vision and its failure in amblyopia

2020 ◽  
Vol 117 (11) ◽  
pp. 6156-6162
Author(s):  
Samuel Eckmann ◽  
Lukas Klimmasch ◽  
Bertram E. Shi ◽  
Jochen Triesch
Keyword(s):  
Eye Movements ◽  
Binocular Vision ◽  
Coding Theory ◽  
Critical Period ◽  
Receptive Fields ◽  
Efficient Coding ◽  
Coding Efficiency ◽  
Vision Development ◽  
Neural Representations ◽  
Active Binocular Vision

The development of vision during the first months of life is an active process that comprises the learning of appropriate neural representations and the learning of accurate eye movements. While it has long been suspected that the two learning processes are coupled, there is still no widely accepted theoretical framework describing this joint development. Here, we propose a computational model of the development of active binocular vision to fill this gap. The model is based on a formulation of the active efficient coding theory, which proposes that eye movements as well as stimulus encoding are jointly adapted to maximize the overall coding efficiency. Under healthy conditions, the model self-calibrates to perform accurate vergence and accommodation eye movements. It exploits disparity cues to deduce the direction of defocus, which leads to coordinated vergence and accommodation responses. In a simulated anisometropic case, where the refraction power of the two eyes differs, an amblyopia-like state develops in which the foveal region of one eye is suppressed due to inputs from the other eye. After correcting for refractive errors, the model can only reach healthy performance levels if receptive fields are still plastic, in line with findings on a critical period for binocular vision development. Overall, our model offers a unifying conceptual framework for understanding the development of binocular vision.

scholarly journals Panoramic visual statistics shape retina-wide organization of receptive fields

2022 ◽  
Author(s):  
Divyansh Gupta ◽  
Wiktor Mlynarski ◽  
Olga Symonova ◽  
Jan Svaton ◽  
Maximilian Joesch
Keyword(s):  
Coding Theory ◽  
Ganglion Cells ◽  
Receptive Fields ◽  
Visual Space ◽  
Natural Scenes ◽  
Retinal Ganglion ◽  
Natural Scene Statistics ◽  
Coding Efficiency ◽  
Theoretical Predictions ◽  
Experimental Approaches

Visual systems have adapted to the structure of natural stimuli. In the retina, center-surround receptive fields (RFs) of retinal ganglion cells (RGCs) appear to efficiently encode natural sensory signals. Conventionally, it has been assumed that natural scenes are isotropic and homogeneous; thus, the RF properties are expected to be uniform across the visual field. However, natural scene statistics such as luminance and contrast are not uniform and vary significantly across elevation. Here, by combining theory and novel experimental approaches, we demonstrate that this inhomogeneity is exploited by RGC RFs across the entire retina to increase the coding efficiency. We formulated three predictions derived from the efficient coding theory: (i) optimal RFs should strengthen their surround from the dimmer ground to the brighter sky, (ii) RFs should simultaneously decrease their center size and (iii) RFs centered at the horizon should have a marked surround asymmetry due to a stark contrast drop-off. To test these predictions, we developed a new method to image high-resolution RFs of thousands of RGCs in individual retinas. We found that the RF properties match theoretical predictions, and consistently change their shape from dorsal to the ventral retina, with a distinct shift in the RF surround at the horizon. These effects are observed across RGC subtypes, which were thought to represent visual space homogeneously, indicating that functional retinal streams share common adaptations to visual scenes. Our work shows that RFs of mouse RGCs exploit the non-uniform, panoramic structure of natural scenes at a previously unappreciated scale, to increase coding efficiency.

scholarly journals Pseudosparse neural coding in the visual system of primates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sidney R. Lehky ◽  
Keiji Tanaka ◽  
Anne B. Sereno
Keyword(s):  
Neural Coding ◽  
Macaque Monkey ◽  
Implicit Assumption ◽  
Data Set ◽  
Efficient Coding ◽  
Population Responses ◽  
Lateral Intraparietal Cortex ◽  
Neural Populations ◽  
Neurophysiological Data ◽  
The Mean

AbstractWhen measuring sparseness in neural populations as an indicator of efficient coding, an implicit assumption is that each stimulus activates a different random set of neurons. In other words, population responses to different stimuli are, on average, uncorrelated. Here we examine neurophysiological data from four lobes of macaque monkey cortex, including V1, V2, MT, anterior inferotemporal cortex, lateral intraparietal cortex, the frontal eye fields, and perirhinal cortex, to determine how correlated population responses are. We call the mean correlation the pseudosparseness index, because high pseudosparseness can mimic statistical properties of sparseness without being authentically sparse. In every data set we find high levels of pseudosparseness ranging from 0.59–0.98, substantially greater than the value of 0.00 for authentic sparseness. This was true for synthetic and natural stimuli, as well as for single-electrode and multielectrode data. A model indicates that a key variable producing high pseudosparseness is the standard deviation of spontaneous activity across the population. Consistently high values of pseudosparseness in the data demand reconsideration of the sparse coding literature as well as consideration of the degree to which authentic sparseness provides a useful framework for understanding neural coding in the cortex.

scholarly journals Stereoscopic depth adaptation from binocularly correlated versus anti-correlated noise: Test of an efficient coding theory of stereopsis

2020 ◽  
Vol 166 ◽  
pp. 60-71 ◽  
Author(s):  
Frederick A.A. Kingdom ◽  
Karl-Christopher Yared ◽  
Paul B. Hibbard ◽  
Keith A. May
Keyword(s):  
Coding Theory ◽  
Stereoscopic Depth ◽  
Correlated Noise ◽  
Efficient Coding ◽  
Noise Test

scholarly journals Activity Patterns of Interneurons in the Caudal Ganglion of the Crayfish

1960 ◽  
Vol 43 (3) ◽  
pp. 655-670 ◽  
Author(s):  
Donald Kennedy ◽  
James B. Preston
Keyword(s):  
Activity Patterns ◽  
Receptive Fields ◽  
Synaptic Activity ◽  
Constant Frequency ◽  
Natural Stimuli ◽  
Synaptic Potentials ◽  
Tactile Stimuli ◽  
Afferent Inputs ◽  
Degree Of Convergence ◽  
High Degree

Responses of ascending interneurons from the caudal ganglion of crayfish have been recorded from single units isolated by dissection from the ventral nerve cord; in addition, post-synaptic activity within the ganglionic neuropile has been studied with intracellular micropipettes. The following classes of interneurons have been found: (1) Large fibers which responded to tactile stimuli with single spikes or phasic bursts. These units usually showed broad receptive fields; and spontaneous activity, when present, showed transitory depressions following responses to natural stimuli. (2) A group of fibers, including many small ones, which responded to proprioceptive stimuli with tonic discharges of varying adaptation rate. (3) Interneurons which showed responses both to tactile stimuli and to activation of the sixth ganglion photoreceptor; and (4) units with constant frequency discharges which were unmodifiable by any of the above afferent inputs. Intracellular recording of post-synaptic activity has shown (1) that widely graded excitatory post-synaptic potentials occur; (2) that multiple firing from single synaptic potentials is usual; (3) that the post-synaptic responses to phasic natural stimuli and to electrical stimulation of ganglionic roots are similar. The existence of widely graded post-synaptic potentials and of extensive receptive fields suggests a high degree of convergence from primary afferents to interneurons. The activation of such post-synaptic units involves integrative synaptic transfer, without 1:1 correspondence between pre- and post-fiber activity.

Wavelet-Like Receptive Fields Emerges by Non-Linear Minimization of Neuron Error

2003 ◽  
Vol 13 (02) ◽  
pp. 87-91
Author(s):  
Allan Kardec Barros ◽  
Andrzej Cichocki ◽  
Noboru Ohnishi
Keyword(s):  
Neural Coding ◽  
Single Neuron ◽  
Receptive Fields ◽  
Natural Images ◽  
Squared Error ◽  
Redundancy Reduction ◽  
One Step ◽  
The One ◽  
Mutually Independent ◽  
Large Research

Redundancy reduction as a form of neural coding has been since the early sixties a topic of large research interest. A number of strategies has been proposed, but the one which is attracting most attention recently assumes that this coding is carried out so that the output signals are mutually independent. In this work we go one step further and suggest an strategy to deal also with non-orthogonal signals (i.e., ''dependent'' signals). Moreover, instead of working with the usual squared error, we design a neuron where the non-linearity is operating on the error. It is computationally more economic and, importantly, the permutation/scaling problem10 is avoided. The framework is given with a biological background, as we avocate throughout the manuscript that the algorithm fits well the single neuron and redundancy reduction doctrine.5 Moreover, we show that wavelet-like receptive fields emerges from natural images processed by this algorithm.

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